We elaborate on a general framework composed of a set of computational tools to accurately quantificate cellular position and gene expression levels throughout early zebrafish embryogenesis captured over a time-lapse series of in vivo 3D images. Our modeling strategy involves nuclei detection, cell geometries extraction, automatic gene levels quantification and cell tracking to reconstruct cell trajectories and lineage tree which describe the animal development. Each cell in the embryo is then precisely described at each given time t by a vector composed of the cell 3D spatial coordinates (x; y; z) along with its gene expression level g. This comprehensive description of the embryo development is used to assess the general connection between genetic expression and cell movement. We also investigate genetic expression propagation between a cell and its progeny in the lineage tree. More to the point, this paper focuses on the evolution of the expression pattern of transcriptional factor goosecoid (gsc) through the gastrulation process between 6 and 9 hours post fertilization (hpf

Bourgine, Paul

Castro González, Carlos

Desnoulez, Sophie

Douloquin, Louise

Ledesma Carbayo, María Jesús

Luengo Oroz, Miguel Ángel

Melani, Camilo

Peyrieras, Nadine

Santos Lleo, Andres de

Savy, Thierry

English

Servicio de Coordinación de Bibliotecas de la Universidad Politécnica de Madrid

Towards a digital model of zebrafish embryogenesis. 
Integration of cell tracking and gene expression quantiflcation 
Carlos Castro-González*, Miguel Ángel Luengo-Oroz*, Louise Douloquinf, Thierry Savyt, Camilo Melania 
Sophie Desnoulez^, María Jesús Ledesma-Carbayo*, Paul Bourgine^, Nadine Peyriéras11, Andrés Santos* 
Abstract—We elabórate on a general framework composed 
of a set of computational tools to accurately quantificate cellular 
position and gene expression levéis throughout early zebrafish 
embryogenesis captured over a time-lapse series of in vivo 
3D images. Our modeling strategy involves nuclei detection, 
cell geometries extraction, automatic gene levéis quantiflcation 
and cell tracking to reconstruct cell trajectories and lineage 
tree which describe the animal development. Each cell in the 
embryo is then precisely described at each given time t by a 
vector composed of the cell 3D spatial coordinates (x,y,z) along 
with its gene expression level g. This comprehensive description 
of the embryo development is used to assess the general 
connection between genetic expression and cell movement. We 
also investígate genetic expression propagation between a cell 
and its progeny in the lineage tree. More to the point, this 
paper focuses on the evolution of the expression pattern of 
transcriptional factor goosecoid (gsc) through the gastrulation 
process between 6 and 9 hours post fertilization (hpf). 
I. INTRODUCTION 
Understanding the role of cell dynamics and morpho-
genetic processes during the embryogenesis of living animáis 
is a major question in bio-medical research. Capturing quan-
titative data of gene expression in time and space at the single 
cell resolution becomes then a crucial step to fulfill this goal. 
This topic has been approached by a few big projects [7] 
[3] [6] concerning different animal models: the C. elegans, 
the Drosophila and the mouse brain. Our focus is on the 
zebrafish (Danio rerio) embryo which, due to its embryonic 
optical transparency, quick growth and similarities to human 
differentiated cell types, provides an interesting system to 
study vertébrate development. 
The construction of a spatiotemporal 4D (3D+T) map of 
gene expression in zebrafish development has challenging 
characteristics: there are no anatomical references as there 
are for the mouse brain, development is not stereotyped at 
cellular level as it is in C. elegans, and its developmental 
complexity (cell morphology, morphogenetic movements, 
etc.) is much higher than in the Drosophila case. 
Compared to previous works on zebrafish, Castro et al. [1] 
presented a strategy to créate a 3D map of gene expression 
This work is supported by the Spanish Ministry of Education through its 
FPU grant program. 
*C. Castro-González, M.A. Luengo-Oroz, MJ. Ledesma and A. Santos 
are with Biomedical Image Technologies, ETSIT, Universidad Politécnica de 
Madrid, 28040, Spain. {ccastro, maluengo, mledesma, andres}@die.upm.es 
"L. Douloquin, S. Desnoulez and N. Peyriéras are with DEPSN, CNRS, 
Institut de Neurobiologie Alfred Fessard, Gif-sur-Yvette 91190, France. 
{desnoulez, louise.duloquin, nadine.peyrieras}@inaf.cnrs-gif.fr 
ÍT. Savy, C. Melani and P. Bourgine are with CREA-École Polytechnique, 
París 75015, France. {paul.bourgine}@polytechnique.edu 
levéis while Keller et al. [4] proposed an in toto global 
description of cell dynamics. The protocol introduced in 
this paper includes both an accurate quantiflcation of the 
expression of gene producís and their cellular location, 
together with the possibility to perform a 4D (3D+T) [9] 
cell tracking. This strategy can then offer new insights into 
the relations between gene expression and cell migrations. 
As a result, we get a complete (x, y, z, t, g) description of 
each embryo cell. With simple modiflcations, this systematic 
analysis could be easily extended to further developing stages 
and genetic producís. 
In this context, we applied time-lapse, confocal, bi-photon 
láser scanning microscopy with the aim of observing the 
spatial location and in vivo evolution of gene expression 
data and zebrafish nuclei during the gastrulation period [5] 
between 6 and 9 hours post fertilization (hpf). During this 
period, the transcriptional factor we focused on -named 
goosecoid (gsc)- starts its expression at the dorsal side to 
progressively move up to the animal pole leading to the 
dorsal axis formation (Fig. 2). 
The paper is organized as follows: Section II shows the 
general framework and describes the computational methods 
involved at cell detection, tracking and gene quantiflcation. 
Section III offers details on the employed data set and 
shows the results achieved on global cell migration and the 
particular behavior of 10 representative cells. Finally we 
discuss the results and bibliography. 
II. METHODOLOGY 
Our general framework (Fig. 1), which aims at describing 
each cell by its position at the (x, y, z, t, g) space, includes 
two main processes: 1) An automatic genetic quantiflcation 
scheme that provides us information about the location in 
time and space and levéis of expression of one gene, gsc. 2) 
An automatic lineage reconstruction scheme that provides 
each embryo cell progeny and trajectory which can be 
optionally visually validated. 
A. Nuclei Detection and Tracking. 
We performed a cell nuclei detection based on the numer-
ical solution of a 3D nonlinear advection-diffusion equation 
proposed in [2]. Given the set of identifled nuclei, in [10] we 
implemented an iterative greedy algorithm that builds the cell 
lineage tree as follows: The vector fleld of every image It is 
computed by registering it into next image It+i. Based on 
this vector fleld, it is possible to predict the position of each 
Fig. 1. Block diagram depicting the framework. 
detected nuclei at It in It+i, then assign the closest detected 
nuclei in t+1 to be the continuation of its trajectory. Mitosis 
are identified by the Hough transform, which detects dividing 
nuclei -which are not longer spherical-, and by the presence 
at i"t+i of two nearest-neighbors. After visual inspection, the 
experts assessed the classification to have at least a 95% of 
correctly tracked cells per time step. 
B. Cell Geometries Extraction. 
Given that our data set did not dispose of membrane 
images, we used the detected nuclei centers as seeds to gen-
érate fheir corresponding Voronoi regions wifhin the embryo 
volume. The Voronoi región associated to each nuclei n, 
can be described as the set of points which are closer to 
rii fhan to any ofher nuclei, (Fig. 1). In [8] we showed that 
these Voronoi regions, compared against a watershed-based 
segmentation, can provide an approximate model of the true 
cell geometries. 
C. Automatic Gene Quantification. 
We assumed the gene expression level to be proportional 
to the fluorescence brightness within the embryo. Therefore, 
our approach was to use the mean intensity valué of the 
gsc channel within each segmented cell in order to assign 
fhem a genetic activity score ranging from 0 to 1. However, 
there are many factors that can significantly bias these scores. 
In consequence, we performed two kinds of normalization: 
first, in order to compénsate for differences on the depth 
extinction and object transparency, we weighted our cell 
scores by the luminosity of fheir corresponding nuclei (which 
is supposed to keep constant at all cases). Then, to enable 
comparison across acquisitions with different gain, saturation 
and offset conditions, we applied an intra-step normalization, 
meaning that valúes assigned to each cell were not absolute 
6 hpf 7.5 hpf 9 hpf 
Fig. 2. Nuclei (top) and gsc (bottom) channels at 6, 7.5 and 9 hpf. 
but dependent on how fhey rated against the rest of the cells 
present at fheir same time stage. 
D. Validation. 
We used the visualization tool Movit [11] which was 
specifically designed to visualize and validate the described 
data. Its validation unit includes the possibility of correcting 
false positives (removing nuclei), false negatives (adding 
nuclei) and adjusting nuclei positions. It is also possible to 
verify cells lineage by validating the links between fheir tem-
poral trajectories, removing false mitosis, creating new links 
to manually added nuclei, etc. This utility was employed to 
label and verify the trajectories of 10 clones (10 cells togefher 
with all their progeny) which were used to study in detail 
the evolution of gsc expression and cell speed fhroughout the 
cell lineage (Section III-C). 
III. EXPERIMENTS 
A. Image Acquisition 
The image data set used in this paper was acquired by 
a bi-photon láser scanning microscope Leica SP5 with a 
10X objective and 1030nm and 980nm láser light excitation 
which resulted in two differentiated channels: one holding 
the gsc expression in the zebrafish, the other holding the 
embryo nuclei. We developed a new GFP transgenic line to 
label the gsc expression, while the nuclei were labeled with a 
H2bmCherry mRNA injection. The resulting 3D stacks are 
composed of a set of 2D images, homogeneously spaced 
in depth, acquired in the XY plañe from the animal pole. 
Image size was 512x512x245 voxels with a voxel resolution 
of 1.51x1.51x1.51 /xm. The repetition of this imaging process 
once every 2m44secs generated a sequence in time depicting 
the embryo in vivo development between 6 and 9 hpf (Fig. 2). 
B. Results: Statistical Global Patterns 
Following the scheme depicted in Fig. 1 our gene quan-
tification strategy was employed to obtain measures of the 
gsc levéis of expression at every cell detected between 6 and 
7hpf 7.5 hpf 8hpf 9 hpf 
Fig. 3. Top row. Gsc levéis of expression, each colored dot stands up for one cell. Bottom row. Speed modules, each colored dot stands up for one current 
cell whereas the colored lines represent their future positions. Views are taken from underneath the animal pole. 
9 hpf. Likewise, our cell tracking strategy let us compute the 
current speed and direction of each cell during that period 
(Fig. 3). After comparing both gsc expression and cell speed, 
we can observe (Fig. 3) that movements towards the dorsal 
side -the site where gsc starts its expression- start with cells 
sited at the opposite ventral side which do not express gsc 
at first. However, as the time goes by, we can appreciate that 
there is a crescent connection between those cells expressing 
gsc and those moving quickly together to the animal pole. 
Studying cells speed vectors altogether with their levéis 
of gsc expression pointed out that cells expressing gsc tend 
to move fast and they do it together in the same direction. 
Their speed vectors steered up coordinate and progressively 
from bottom to top as the gsc expression evolved from the 
dorsal side to the animal pole. 
C. Results: Linking Lineage and Cell Gene Expression 
Ten clones, chosen to representatively sample the cell 
migration, were manually validated and labeled, including 
the six children derived from their mitosis, in order to 
accurately illustrate the evolution of gsc expression and cell 
speed across the lineage tree. Fig. 4 shows the complete cell 
trajectories of the corrected cells indicating their respective 
starting and end points. Some representative results, shown 
in Fig. 5, indicate for instance the fact that the gene level of 
expression is not constant throughout the cell life. This sug-
gests that gsc expression does not follow a clonal behavior 
but it is rather dynamical since one cell can turn on and off 
the gene along its life. As a consequence, the gene evolution 
in time can not be adequately conserved by just propagating 
its expression through the cell tracking. In other words, gsc 
level of expression is neither constant ñor restricted to the 
same cell population during the embryo development and do 
not strictly follow a clonal descendance behavior. 
Fig. 4. Complete trajectories of the 10 validated cells from their start (s) at 
6hpf to their end (e) at 9hpf. View taken from underneath the animal pole. 
IV. CONCLUSIONS AND FUTURE WORK 
We have proposed a general framework that uses a set 
of computational tools to automatically extract all cells 
trajectories and their progeny as well as their quantitative 
levéis of expression of gsc in a zebrafish embryo undergoing 
gastrulation between 6 and 9 hpf. The resulting knowledge 
of the (x, y, z, t, g) space let us establish implications about 
how genetic expression relates to the cell movements and 
descendants. For instance, the collective nature of cell migra-
tion was established as well as the fact that gene expression is 
not deterministically attached to progeny links. We detected 
cells flocking into the área where gsc was expressing even 
though they were not yet expressing gsc. It appears, thus, that 
Evolution of the gsc expression at cells 1,9,10 
100 i 25 
Evolution of the speed at cells 1, 9, 10 
6.5 fipf 7 hpl 7.5 tlpf S hpf 8.5 hpf 9 hpl 
Evolution of the gsc expression at cell S 
6.5 hpf 7 hpf 7.5 hpf S hpf 8,5 hpl 
Evolution of the speed at cell 5 
i hit 
5> 80 
O 
'i 
—mnther cell 
child. 
—molher cell 
...chitó. 
#fl( 
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- T J 2 5 / \ chitó 
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Evolution of the gsc expression at cell 7 
9 hpf 6.5 hpf 7 hpf 7.5 hpf 8 hpf 6.5 hpf 
Evolution of the speed at cell 7 
9 hpf 
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child. 
A-- M vs VI \ 
6.5 hpf 5 hpf 7 hpf 7.5 hpf 8 hpf 
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Fig. 5. Relationship between the levéis of gsc expression and speed. The top row shows 3 cell trajectories with no mitosis: Cell 1, placed at the head of 
the migrating population shows a strong gsc expression from the beginning at 6 hpf to later on decay when the gene has already reached the animal pole 
at 9 hpf. Cell 9, placed in the middle of the migrating population, only shows gsc transcriptional activity during a short period, centered around 7.5 hpf, 
to rapidly decay afterwards when the gsc pattern moves ahead into the animal pole. Cell 10, placed at the tail of the migrating population only starts to 
timidly show gsc activity around 8 hpf when it flocks into the rear part of the gsc domain. In the three cases we can see a connection between their levéis 
of gsc expression and their speed modules. The middle row shows cell 5 undergoing mitosis at 6.7 hpf. Being placed on the margin of the gsc domain, 
one of the children cells starts expressing gsc and completes its migration to the animal pole whereas the other do not show any expression and ends up 
moving slower and finishing its migration earlier. The bottom row shows cell 7 undergoing mitosis at 6.7 hpf as well. Being placed on the central área of 
the gsc domain, both children show similar levéis of gsc expression and follow resembling speeds and trajectories to the animal pole in this case. 
gene expression does not exclusively follow cell movements: 
gsc transcription can be active at one cell trajectory for a 
while, then vanish. Therefore, just propagating gene expres-
sion through the tracked itineraries of detected cells do not 
appear to be an accurate way to model its domain evolution. 
Finally, the proposed scheme can be easily extended to 
further gene producís and developmental stages. Future work 
includes adding múltiple gene expressions into the same in 
vivo embryo to study further relationships among them and 
creating fate maps for cells in later developmental stages. 
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eras, and A. Santos, "An automatic quantification and registration 
strategy to créate a gene expression atlas of zebrafish embryogenesis," 
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[8] M. Luengo-Oroz, L. Duloquin, C. Castro, T. Savy, E. Faure, B. Lom-
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Towards a digital model of zebraﬁsh embryogenesis.

Integration of Cell Tracking and Gene Expression Quantiﬁcation

Archivo Digital UPM

A 3D digital atlas of C. elegans and its application to single-cell analyses,&quot;

An automatic quantification and registration strategy to créate a gene expression atlas of zebrafish embryogenesis,&quot;

Can voronoi diagram model cell geometries in early sea-urchin embryogenesis?&quot;

Cells tracking in a live zebrafish embryo,&quot; in

Estímate of the cell number growth rate using PDE methods of image processing and time series analysis,&quot;

Genome-wide atlas of gene expression in the adult mouse brain,&quot;

Imaging in Systems Biology,&quot;

MOVIT: Morphogenesis Visualization Tool,&quot; unpublished,

quantitative spatiotemporal atlas of gene expression in the Drosophila blastoderm,&quot;

Reconstruction of Zebrafish Early Embryonic Development by Scanned Light Sheet Microscopy,&quot;

Stages of embryonic development of the zebrafish,&quot;

http://oa.upm.es/6941/4/INVE_MEM_2010_75624.pdf

Towards a digital model of zebraﬁsh embryogenesis. Integration of Cell Tracking and Gene Expression Quantiﬁcation

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